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Quantifying leaf-trait covariation and its controls across climates and biomes.

  • Yang, Yanzheng1, 2, 3
  • Wang, Han1, 3
  • Harrison, Sandy P3, 4
  • Prentice, I Colin1, 3, 5, 6
  • Wright, Ian J6
  • Peng, Changhui3, 7
  • Lin, Guanghui1, 8
  • 1 Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China. , (China)
  • 2 Joint Center for Global Change Studies (JCGCS), Beijing, 100875, China. , (China)
  • 3 State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Forestry, Northwest A&F University, Yangling, Shaanxi, 712100, China. , (China)
  • 4 School of Archaeology, Geography and Environmental Sciences (SAGES), University of Reading, Reading, RG6 6AH, UK.
  • 5 AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, SL5 7PY, UK.
  • 6 Department of Biological Sciences, Macquarie University, North Ryde, NSW, 2109, Australia. , (Australia)
  • 7 Department of Biological Sciences, Institute of Environmental Sciences, University of Quebec at Montreal, C.P. 8888, Succ. Centre-Ville, Montréal, H3C 3P8, QC, Canada. , (Canada)
  • 8 Key Laboratory of Stable Isotope and Gulf Ecology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, 518055, China. , (China)
Published Article
New Phytologist
Wiley (Blackwell Publishing)
Publication Date
Jan 01, 2019
DOI: 10.1111/nph.15422
PMID: 30272817


Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis (PCA) was used to characterize trait variation, redundancy analysis (RDA) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life-form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area (LA), internal-to-ambient CO2 ratio (χ), leaf economics spectrum traits (specific leaf area (SLA) versus leaf dry matter content (LDMC) and nitrogen per area (Narea )), and photosynthetic capacities (Vcmax , Jmax at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life-form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.

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